EarCalo: Earable-Based Energy Expenditure Estimation While Running

Abstract

Accurately estimating energy expenditure (EE) is crucial for understanding exercise efficiency, managing fitness goals, and monitoring health conditions. Existing wearable systems either rely primarily on heart rate and motion sensors, resulting in unsatisfactory accuracy, or require bulky setups such as thermal cameras to improve performance by integrating more physiological information, which limits real-world applicability. We present EarCalo, an earable-based system that leverages in-ear audio sensing to estimate EE during running. The system extracts airflow-induced acoustic variations within the ear canal and employs a deep neural network to translate these subtle in-ear sound dynamics into EE estimates. The key insight is that in-ear acoustic signals can capture multiple physiological factors such as breathing and cardiovascular activity, while also reflecting motion-related cues like running intensity. These rich acoustic cues are closely related to EE and provide a unified sensing modality for estimation. We evaluated EarCalo on 21 participants running at varying speeds using a mixed-user setting. EarCalo achieved a mean absolute error (MAE) of 0.67 kcal/min, a mean absolute percentage error (MAPE) of 11.98%, and a Pearson correlation of 0.945, which achieves accuracy close to established physiological standards. This work represents an early step toward practical and personalized earable-based EE estimation in everyday settings.